A new SaferAI report finds Z.ai's open-weight GLM-5.2 approaches frontier AI capabilities while lacking key safety mitigations, renewing concerns that powerful open models could ou…

As policymakers debate how to govern increasingly powerful AI systems like OpenAI’s GPT-5.6 Sol and Anthropic’s Mythos, a Chinese open-weight model has narrowed the gap with the industry’s leaders. GLM-5.2, the open-weight AI model from China’s Z.ai, is only a few months behind OpenAI’s GPT-5.5 and Anthropic’s Claude Opus 4.7 on cyber and bio capabilities, according to a new report from AI safety nonprofit SaferAI. But the divide between frontier capabilities and safety practices is growing. According to SaferAI’s evaluation, which the nonprofit ran via Z.ai’s public API, GLM-5.2 refused none of the offensive cyber or dual-use biology tasks it was given. By comparison, Claude Opus 4.7 “refused so consistently that SaferAI could not complete CyberGym on it at all.” (CyberGym is a benchmark that evaluates cybersecurity capabilities. OpenAI used it in the evaluation that preceded last month’s Hugging Face breach.) It’s a stark reminder of what some critics have warned for years: that open-weight AI models could put highly capable AI into the hands of potential attackers, with no way to police how they use the technology once they download the weights. With open-weight models rapidly approaching the capabilities of the world’s leading AI systems, the debate is moving from whether they can compete to how society manages risks once they are released. “The frontier of capability is not the frontier of risk, and so we do have to take into account the state of the mitigations as well to assess the risk properly,” Henry Papadatos, executive director of SaferAI, told TechCrunch. While Z.ai could apply safety measures to its hosted API, those protections become unenforceable once someone runs the weights on their own hardware, where they can remove or modify any safeguards, fine-tune the models, or change system prompts. Frontier developers like OpenAI and Anthropic tend to rely on safeguards like classifiers, refusal training, and API-level controls to limit dangerous cyber and biological assistance. Those measures are far from foolproof: jailbreaks routinely bypass protections on deployed models. Far.ai, an AI safety nonprofit, found hundreds of universal jailbreaks — defined as reusable keys that succeed on most harmful requests — in frontier models like xAI’s Grok 4.5 and Google DeepMind’s Gemini 3.1 Pro. According to the report, jailbreaks succeed when attackers combine multiple manipulation techniques — including roleplaying, authority impersonation, fake conversation history, and follow-up prompts — to amplify weak points in a model’s defenses.